Disturbance through human hunting activity can significantly impact prey species through both consumptive and nonconsumptive effects. The nonconsumptive effects of rabbit hunting on Northern Bobwhite (Colinus virginianus; hereafter, bobwhite) could cause an increased perceived risk of predation by bobwhite during rabbit hunting events may elicit anti-predator responses, such as reduced movement away from the safety of cover.
bobwhite <- read.csv('bobwhite3.csv')
bobwhite$ID <- as.factor(bobwhite$ID) #make ID a factor
p1<- ggplot(bobwhite, aes(x=HuntDay, y=HW_Dist, group=ID, color=ID, shape=ID)) +
geom_point(size=4, alpha=0.6, position = position_dodge2(width=.33, preserve = "total")) +
scale_y_continuous() +
#geom_line() +
geom_smooth(method = "lm", se = FALSE) +
labs(title="Risk Behavior in Bobwhite During Hunting Season", x= "Hunting Season Species", y = "Distance from Hardwood Forest Cover (meters)")+
theme_bw()+
scale_color_brewer(palette = "BrBG")
p1
## `geom_smooth()` using formula 'y ~ x'
# ID: Unique ID given to each bobwhite covey tracked in chronological order.
# HuntDay: Denotes if it was a “Rabbit” or “Quail” (bobwhite) scheduled hunt day.
# HW_Dist: Distance in meters a bobwhite covey was from hardwood habitat.
bobwhite_means <- bobwhite %>%
group_by(HuntDay) %>%
summarise(mean_HW_Dist=mean(HW_Dist),
se_HW_Dist=sd(HW_Dist)/sqrt(n()))
bobwhite_means
## # A tibble: 2 × 3
## HuntDay mean_HW_Dist se_HW_Dist
## <chr> <dbl> <dbl>
## 1 Quail 22.3 4.29
## 2 Rabbit 57.0 5.30
mixed_bobwhite <- lmer(HW_Dist~(HuntDay*ID)+(1|ID), data = bobwhite)
## Warning in as_lmerModLT(model, devfun): Model may not have converged with 1
## eigenvalue close to zero: 5.5e-09
anova(mixed_bobwhite)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## HuntDay 19204.9 19204.9 1 188 11.0107 0.001088 **
## ID 3168.5 633.7 5 188 0.3633 0.873151
## HuntDay:ID 23341.2 4668.2 5 188 2.6764 0.023103 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::check_model(mixed_bobwhite)
bob_means <- emmeans(mixed_bobwhite, "HuntDay")
## Cannot use mode = "kenward-roger" because *pbkrtest* package is not installed
## NOTE: Results may be misleading due to involvement in interactions
bob_means
## HuntDay emmean SE df lower.CL upper.CL
## Quail 23.1 25.3 188 -26.92 73.1
## Rabbit 45.1 25.1 188 -4.31 94.5
##
## Results are averaged over the levels of: ID
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
Because there is an unequal number of points per day for each individual covey, we can check the accuracy of this model by using emmeans:
em_bob <- as.data.frame(bob_means)
em_bob
## HuntDay emmean SE df lower.CL upper.CL
## 1 Quail 23.08173 25.34776 188 -26.920856 73.08431
## 2 Rabbit 45.11574 25.05572 188 -4.310749 94.54224
bobwhite_means
## # A tibble: 2 × 3
## HuntDay mean_HW_Dist se_HW_Dist
## <chr> <dbl> <dbl>
## 1 Quail 22.3 4.29
## 2 Rabbit 57.0 5.30
em_bob$HuntDay <- factor(em_bob$HuntDay, levels= c("Quail","Rabbit"))
ggplot(em_bob, aes(x=HuntDay, y=emmean)) +
geom_point(size=5, color="#87b8d3") +
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=.2, color="#6699CC") +
geom_point(size=5, data=bobwhite_means, x=bobwhite_means$HuntDay, y=bobwhite_means$mean_HW_Dist, color = "#336699") +
theme(axis.text.x = ggtext::element_markdown(color = "tan4", size = 12)) +
scale_x_discrete(labels = labels) +
theme(plot.caption=element_text(size=9, hjust=0, margin=margin(15,0,0,0)))+
theme_bw()+
labs(title="Comparing raw means to emmeans", caption="Light blue = raw means, Dark blue = adjusted means", x= "Hunting Day (1=Quail, 2=Rabbit)", y = "Average distance from hardwood cover (m)")
\(~\) \(~\)
Nested Hierarchical Model
Great Tits (Parus major)
Advertisement signaling is usually linked to intersexual selection and intrasexual competition and thus is a key component of a species’ ecology. Using a novel spatial tracking system, the authors tested whether or not the spatial behavior of male and female great tits (Parus major) changes in relation to the response of a territorial male neighbor to an intruder. They tracked the spatial behavior of male and female great tits (N = 13), 1 hr before and 1 hr after simulating territory intrusions. The individual bird is a random effect, and the sex of the bird and measurements taken before and after are fixed effects.
tit <- read.csv("great_tits.csv")
tit$sex <- as.factor(tit$sex)
ggplot(tit, aes(sex, dist_m, colour = as.factor(id), shape=as.factor(measure))) +
geom_jitter(width =0.15, size=5, alpha=0.6)+
ylab ("Response Distance from Playback Location (meters)") +
xlab ("Sex") +
annotate("text", x = 2, y = 76, label = "13 birds") +
annotate("text", x = 2, y = 72, label = "2 measurements per bird") +
annotate("text", x = 2, y = 67, label = "26 total measurements", size=4, color="blue")+
labs(title="Sex-specific Responses to Territorial Intrusions", caption="1 = female
2 = male")+
scale_shape_discrete(name= "Measurements
Before / After")+
theme_bw()
In this study, 13 individuals were measured 2 times, 1 hour before playback and 1 hour after. The individuals are separated by sex to look at the different distances between females and males. Distance on the Y axis is measured in meters, with distance being how many meters away a bird was detected before and after a playback recording was played of a territorial male’s song. Based on the measurements of distance before and after playback, it looks like there isn’t really a pattern of whether or not sex plays a role in an individual bird’s movement after a territorial playback.
lmmtit <- lmer(dist_m ~ sex + (1|sex/id), data = tit)
summary(lmmtit)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dist_m ~ sex + (1 | sex/id)
## Data: tit
##
## REML criterion at convergence: 220.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3793 -0.5409 -0.0890 0.4035 1.6695
##
## Random effects:
## Groups Name Variance Std.Dev.
## id:sex (Intercept) 258.264 16.071
## sex (Intercept) 4.187 2.046
## Residual 99.407 9.970
## Number of obs: 28, groups: id:sex, 14; sex, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 28.838 6.533 14.604 4.414 0.000534 ***
## sex2 -2.629 9.909 14.236 -0.265 0.794568
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## sex2 -0.659
## optimizer (nloptwrap) convergence code: 0 (OK)
## unable to evaluate scaled gradient
## Hessian is numerically singular: parameters are not uniquely determined
Using a linear mixed model to see if there’s an interaction between the sex of the bird and distance of detection.
anova(lm(dist_m ~ sex/id, data = tit))
## Analysis of Variance Table
##
## Response: dist_m
## Df Sum Sq Mean Sq F value Pr(>F)
## sex 1 47.4 47.40 0.1309 0.7207
## sex:id 2 90.6 45.31 0.1251 0.8830
## Residuals 24 8692.3 362.18